Hindi

Vietnamese

This document aims to track the progress in Natural Language Processing (NLP) and give an overview
of the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets.

It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging
as well as more recent ones such as reading comprehension and natural language inference. The main objective
is to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for their
task of interest, which serves as a stepping stone for further research. To this end, if there is a
place where results for a task are already published and regularly maintained, such as a public leaderboard,
the reader will be pointed there.

Contributing

Guidelines

Results Results reported in published papers are preferred; an exception may be made for influential preprints.

Datasets Datasets should have been used for evaluation in at least one published paper besides
the one that introduced the dataset.

Code We recommend to add a link to an implementation
if available. You can add a Code column (see below) to the table if it does not exist.
In the Code column, indicate an official implementation with Official.
If an unofficial implementation is available, use Link (see below).
If no implementation is available, you can leave the cell empty.

Adding a new result

If you would like to add a new result, you can just click on the small edit button in the top-right
corner of the file for the respective task (see below).

This allows you to edit the file in Markdown. Simply add a row to the corresponding table in the
same format. Make sure that the table stays sorted (with the best result on top).
After you’ve made your change, make sure that the table still looks ok by clicking on the
“Preview changes” tab at the top of the page. If everything looks good, go to the bottom of the page,
where you see the below form.

Add a name for your proposed change, an optional description, indicate that you would like to
“Create a new branch for this commit and start a pull request”, and click on “Propose file change”.

Adding a new dataset or task

For adding a new dataset or task, you can also follow the steps above. Alternatively, you can fork the repository.
In both cases, follow the steps below:

If your task is completely new, create a new file and link to it in the table of contents above.

If not, add your task or dataset to the respective section of the corresponding file (in alphabetical order).

Briefly describe the dataset/task and include relevant references.

Describe the evaluation setting and evaluation metric.

Show how an annotated example of the dataset/task looks like.

Add a download link if available.

Copy the below table and fill in at least two results (including the state-of-the-art)
for your dataset/task (change Score to the metric of your dataset). If your dataset/task
has multiple metrics, add them to the right of Score.

Submit your change as a pull request.

Model

Score

Paper / Source

Code

Wish list

These are tasks and datasets that are still missing:

Bilingual dictionary induction

Discourse parsing

Keyphrase extraction

Knowledge base population (KBP)

More dialogue tasks

Semi-supervised learning

Frame-semantic parsing (FrameNet full-sentence analysis)

Exporting into a structured format

You can extract all the data into a structured, machine-readable JSON format with parsed tasks, descriptions and SOTA tables.